A comparative evaluation of name-matching algorithms

Name matching—recognizing when two different strings are likely to denote the same entity—is an important task in many legal information systems, such as case-management systems. The naming conventions peculiar to legal cases limit the effectiveness of generic approximate string-matching algorithms in this task. This paper proposes a three-stage framework for name matching, identifies how each stage in the framework addresses the naming variations that typically arise in legal cases, describes several alternative approaches to each stage, and evaluates the performance of various combinations of the alternatives on a representative collection of names drawn from a United States District Court case management system. The best tradeoff between accuracy and efficiency in this collection was achieved by algorithms that standardize capitalization, spacing, and punctuation; filter redundant terms; index using an abstraction function that is both order-insensitive and tolerant of small numbers of omissions or additions; and compare names in a symmetrical, word-by-word fashion. 1

A General Optimization Technique for High Quality Community Detection in Complex Networks

Recent years have witnessed the development of a large body of algorithms for community detection in complex networks. Most of them are based upon the optimization of objective functions, among which modularity is the most common, though a number of alternatives have been suggested in the scientific literature. We present here an effective general search strategy for the optimization of various objective functions for community detection purposes. When applied to modularity, on both real-world and synthetic networks, our search strategy substantially outperforms the best existing algorithms in terms of final scores of the objective function; for description length, its performance is on par with the original Infomap algorithm. The execution time of our algorithm is on par with non-greedy alternatives present in literature, and networks of up to 10,000 nodes can be analyzed in time spans ranging from minutes to a few hours on average workstations, making our approach readily applicable to tasks which require the quality of partitioning to be as high as possible, and are not limited by strict time constraints. Finally, based on the most effective of the available optimization techniques, we compare the performance of modularity and code length as objective functions, in terms of the quality of the partitions one can achieve by optimizing them. To this end, we evaluated the ability of each objective function to reconstruct the underlying structure of a large set of synthetic and real-world networks.Comment: MAIN text: 14 pages, 4 figures, 1 table Supplementary information: 19 pages, 8 figures, 5 table

Identifying communities by influence dynamics in social networks

Communities are not static; they evolve, split and merge, appear and disappear, i.e. they are product of dynamical processes that govern the evolution of the network. A good algorithm for community detection should not only quantify the topology of the network, but incorporate the dynamical processes that take place on the network. We present a novel algorithm for community detection that combines network structure with processes that support creation and/or evolution of communities. The algorithm does not embrace the universal approach but instead tries to focus on social networks and model dynamic social interactions that occur on those networks. It identifies leaders, and communities that form around those leaders. It naturally supports overlapping communities by associating each node with a membership vector that describes node's involvement in each community. This way, in addition to overlapping communities, we can identify nodes that are good followers to their leader, and also nodes with no clear community involvement that serve as a proxy between several communities and are equally as important. We run the algorithm for several real social networks which we believe represent a good fraction of the wide body of social networks and discuss the results including other possible applications.Comment: 10 pages, 6 figure

Active case-based reasoning for lessons delivery systems

Paper presented at The 13th International Florida Artificial Intelligence Research Society Conference, FLAIRS 1999, Menlo Park, FL: pp. 170-174.Exploiting lessons learned is a key knowledge management (KM) task. Currently, most lessons learned systems are passive, stand-alone systems. In contrast, practical KM solutions should be active, interjecting relevant information during decision-making. We introduce an architecture for active lessons delivery systems, an instantiation of it that serves as a monitor, and illustrate it in the context of the conversational case-based plan authoring system HICAP (Muñoz-Avila et al., 1999). When users interact with HICAP, updating its domain objects, this monitor accesses a repository of lessons learned and alerts the user to the ramifications of the most relevant past experiences. We demonstrate this in the context of planning noncombatant evacuation operations

Molecular Analysis of Microbial Communities in Endotracheal Tube Biofilms

Ventilator-associated pneumonia is the most prevalent acquired infection of patients on intensive care units and is associated with considerable morbidity and mortality. Evidence suggests that an improved understanding of the composition of the biofilm communities that form on endotracheal tubes may result in the development of improved preventative strategies for ventilator-associated pneumonia. (n = 5). DGGE profiling of the endotracheal biofilms revealed complex banding patterns containing between 3 and 22 (mean 6) bands per tube, thus demonstrating the marked complexity of the constituent biofilms. Significant inter-patient diversity was evident. The number of DGGE bands detected was not related to total viable microbial counts or the duration of intubation.Molecular profiling using DGGE demonstrated considerable biofilm compositional complexity and inter-patient diversity and provides a rapid method for the further study of biofilm composition in longitudinal and interventional studies. The presence of oral microorganisms in endotracheal tube biofilms suggests that these may be important in biofilm development and may provide a therapeutic target for the prevention of ventilator-associated pneumonia

Research in progress: report on the ICAIL 2017 doctoral consortium

This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences